Authors: Daniel Karikari Frempong, Mutala Nakpan Jentina, Hannah Owusu Ansah, Gabriel Oduro Asirifi

Abstract: The pivotal role of intelligent sensors in building and running smart homes is discussed in this literature review. First, we present a brief overview of smart homes and intelligent sensors, emphasizing the critical importance of this sophisticated technology used to transform ordinary homes into intelligent AI-controlled houses. The review then delves into the principles of several types of intelligent sensors, including energy, health and wellbeing, environmental, security, and appliance sensors. Besides playing a critical role in gathering data for personalized home automation services, this section touches upon their remarkable contribution to sustainable living, energy-saving, and human wellbeing. The review next examines key technologies and standards that enable seamless communication between devices, such as Matter, Wi-Fi, and Zigbee. This section also sheds light on how artificial intelligence and machine learning could change the paradigm of processing information collected by these intelligent sensors, leading to advanced predictive analysis and decision-making. Finally, we propose ways to address some challenges that impede the widespread application of intelligent sensors, such as interoperability, security, privacy concerns, and affordability. We also present promising avenues for future research on intelligent sensors for smart living, such as increased autonomy, advanced sensor miniaturization, and human-centric design.

DOI: https://doi.org/10.5281/zenodo.20080909

Published by: vikaspatanker

Authors: Aziz A. Isra, Chaturvedi Rachna, Prakash Jyoti

Abstract: Chromium (Cr) metal and Cr compounds are primarily used in applications like making stainless steel, polishing, and leather tanning. Chromium naturally occurs in air, water, rocks, and soil, via natural or anthropogenic sources. It exists in different oxidation states ranging from +6 to -2. The most stable forms are the trivalent Cr(III) and the hexavalent Cr (VI), which are interconvertible with each other. Chromium is an important trace element for human beings as it stimulates the breakdown of fatty acids and cholesterol. However, if exposed to a higher dose of chromium particles for a longer period, it can lead to human health toxicity and fatality. It is introduced into the environment through chemical and physical processes or even by biological transport systems in living organisms. Over the past decades, chromium contamination has become a significant threat with a negative influence on the environment, especially soil and water, and its accumulation affects human health, plant metabolism, and animal tissues. By gathering information from various published literature, we have highlighted the adversities caused by Chromium toxicity, for example, acute and chronic toxicity among human beings like carcinogenic potential, apoptosis, oxidative stress, and DNA adducts. This review focuses on the complex chemistry of chromium, its exposure routes, and hazardous effect of chromium on human health, and the mechanism of chromium toxicity upon entering the cells. Therefore, it is now important to investigate and develop various useful sustainable remediation strategies to balance and reduce the increased levels of chromium in the environment.

DOI: https://doi.org/10.5281/zenodo.20075414

Published by: vikaspatanker

Authors: Research Scholar Udaya Kumar Nanubala, Professor Dr.Pankaj Khairnar

Abstract: Emotion recognition has surely become an important research field in artificial intelligence because it can improve how humans interact with computers. Moreover, this technology helps in building better intelligent systems. Basically, traditional methods using single type of data fail to understand human emotions properly because emotions are expressed through multiple ways – text, speech, and facial expressions – all at the same time. This paper actually reviews transformer deep learning models that definitely work with different types of data for recognizing emotions. This study looks at how emotion recognition methods have changed from old rule-based and machine learning ways to new deep learning and transformer systems. As per the research, regarding emotion detection techniques, there has been clear progress from basic approaches to advanced methods. Basically, deep learning models like CNNs and RNNs have made feature extraction and pattern recognition better, but the same models struggle with long-range connections and combining different types of data. Basically, Transformer models use attention mechanisms to understand context better and make different types of data work together in the same way. As per recent studies, multimodal transformer systems improve emotion detection by combining different types of data sources into one framework. Regarding performance, this approach gives more accurate and reliable results. As per the review, different multimodal fusion techniques like early, late, and hybrid fusion strategies are analyzed regarding their role in making system performance better. Despite good progress, challenges like different data types, matching different modes, high computing needs, and limited large multimodal datasets remain critical issues that need further attention, as the field itself faces these ongoing problems. Also, this study further identifies important research gaps and emphasizes that efficient fusion mechanisms, scalable architectures, and real-world deployment strategies itself need more development. The findings give important insights for developing better emotion recognition systems that can further improve human-machine interaction itself.

DOI: https://zenodo.org/records/20074005

Published by: vikaspatanker

Authors: Manas Kumar, Heera Sahu, Shivam Jaiswal, Amar Verma , Professor Pushpa Sahu

Abstract: With the mining industry moving from traditional manual methods to the advanced mechanised mining, the focus is also shifting to the energy efficiency of the equipment and system being employed. Most of the equipment used in mining like shovel, drill, elevator, continues miner, conveyor, pumps etc. runs on electricity. Electric energy being the only form of energy which can be easily converted to any other form plays a vital role for the growth of any industry. The Power Factor gives an idea about the efficiency of the system to do useful work out of the supplied electric power. A low value of power factor leads to increase is electric losses and also draws penalty by the utility. Significant savings in utility power costs can be realized by keeping up an average monthly power factor close to unity. To improve the power factor to desired level, reactive power compensators are used in the substations. The most common used device is capacitor bank which are switched on and off manually based on the requirement. If automatic switching can be employed for the correction devices, not only it will improve the response time but also removes any scope for error. The work carried out is concerned with developing power factor correction equipment based on embedded system which can automatically monitor the power factor in the mining electrical system and take care of the switching process to maintain a desired level of power factor which fulfils the standard norms. The Automatic Power Factor Correction (APFC) device developed is based on embedded system having 89S52 microcontroller at its core. The voltage and current signal from the system is sampled and taken as input to measure the power factor and if it falls short of the specified value by utility, then the device automatically switch on the capacitor banks to compensate for the reactive power. The number of capacitors switched on or off is decided by the microcontroller based on the system power factor and the targeted power factor. The measurement and monitoring of three different possible load types suggested that only the inductive loads required power factor correction. After employing the correction equipment the targeted power factor of 0.95 is achieved and the increase in power factor varied from 9% to 19% based on the combination of load. There is also a decrease of 1.7% in the total energy consumption due to reduction in load current. The economic analysis for power factor improvement considering the data from a local coal mine suggested the payback period to be around 9 months if the correction equipment is implemented.

DOI: https://zenodo.org/records/20070425

Published by: vikaspatanker

Authors: Ria Ranjan Kumar, Ms. Mitali Srivastava

Abstract: The growing adoption of Artificial Intelligence and its intervention in the legal sphere has been a welcome sight for everyone in the industry. From judicial institutions to corporations, AI has rapidly shown an overarching effect on judicial functioning. However, with this bargaining effect, what comes is the danger of Artificial intelligence. Due to the lack of regulatory mechanisms and compliance laws, a varied but unexpected stream of jeopardy is underway for all of us. This is somehow related to the growing correspondence being provided by human beings and the pervasive acceptance not just by the elite class of the world but across all classes. The only laws that are currently under the purview of this domain are the Digital Personal Data Protection Act, 2023, for privacy and personal data, the Information Technology Act, 2000 for cyber offences and intermediary liability, and the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021. With this dissertation, I shall try to lay down an intricate evaluation of the recent AI interception and reflect on the dire need to incorporate it with a proper legal regulatory system. Although the term "artificial intelligence" is not used in the DPDP act, one crucial word mentioned therein is "automated," which refers to a digital procedure that can process data automatically. That is the closest the Act gets to discussing artificial intelligence. The act does not specifically address algorithmic bias, deepfakes, facial recognition, automated decision-making, AI explainability, or generative AI training data. There are a lot of inconsistencies in the law itself, and the dystopian tone of the act adds insult to injury.

DOI: https://doi.org/10.5281/zenodo.20069843

Published by: vikaspatanker

Authors: Khushi Wadhwa, Rajat Takkar, Vaani, Kashish Sharma, Kartikay Singh Manhas

Abstract: In the world of medical imaging, Edge artificial intelligence (AI) is driving a revolution by enabling real-time analysis and diagnosis decision making. The aforementioned article examines the constantly developing subject of edge AI-powered healthcare imaging, describing the most recent ad-vancements, creations, and concepts that could transform a variety of medical fields by instantly interpreting medical images, which can be crucial in life-saving circumstances. The Edge AI can be used in remote clinics and other medical imaging situations. In rural diabetes camps, diabetic retinopathy can be diagnosed with Fundus cameras and point-of-care ultrasound without radiologists. In emergency situations, portable X-ray devices can diagnose fractures. The three main types of diagnos-tic procedures—imaging-based, pathology-driven, and protective diagnostic approaches—as well as the alterations and adaptations brought about by the application of Edge AI are also covered in this article. Using medical records raises several ethical issues because they are very sensitive documents. These challenges have also been discussed in this article. The necessity for further developments in Edge AI-based diagnostic techniques is also covered in the article. Additionally, there is a great deal of potential for the future in the creation of tools and techniques that are easy to use and incorporate into routine operations. The increasing usage of clinical decision support systems makes edge AI a promising topic in healthcare and diagnostics. Despite a number of obstacles to its application and adoption, the research concludes that Edge AI in healthcare has a promising future. However, in order to guarantee that facilities are available for this, a high degree of precision must be attained and patients must have better medical outcomes. The potential of AI to transform healthcare and enhance patient outcomes is also highlighted in this paper, with a focus on responsible implementation and ongoing assessment.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Mahantesh M. Ganganallimath, Dr. K. Vizayakumar, Dr. Umesh M. Bhushi

Abstract: By providing cast components to the automobile, aerospace, railroad, construction, defense, and heavy engineering industries, the Indian foundry sector is essential to the manufacturing sector. Casting flaws, process unpredictability, material waste, high rejection rates, energy inefficiency, and growing international competitiveness are some of the industry's major obstacles. In this regard, sustainable industrial growth now depends on quality assurance and quality-centric methods. The necessity of methodical quality assurance procedures, process control systems, and continuous improvement techniques in Indian foundries is examined in this study. The study highlights that quality-driven systems enhance customer satisfaction and product dependability while simultaneously lowering costs and promoting long-term competitiveness and environmental sustainability. The combination of Industry 4.0, automation, and statistical quality tools for stable growth is further supported by recent research on KPI-driven foundry quality systems and sustainable control models.

DOI: https://doi.org/10.5281/zenodo.20068127

Published by: vikaspatanker

Authors: Ch Naga Lakshmi, B Tejaswini, B Anusha, R Nandini, N Tasleem

Abstract: The increasing incidence of chronic illnesses, including cardiovascular and respiratory disorders, has emphasized the importance of continuous health parameter monitoring. Conventional systems for vital sign assessment are primarily hospital-based, costly, and limited to periodic medical consultations, which may delay the detection of abnormal physiological variations. To overcome these limitations, this paper presents a Smart Health Surveillance System designed using Internet of Things (IoT) technology integrated with low-cost biomedical sensors. The proposed model employs a MAX30100 pulse oximeter to measure heart rate and blood oxygen saturation (SpO₂), a DS18B20 digital sensor to record body temperature, and a Node MCU ESP8266 microcontroller to process and transmit data. Measurement outputs are displayed on an LCD screen, while IoT functionality enables remote monitoring through wireless connectivity. Experimental evaluation demonstrates that the system achieves a heart rate accuracy of ±3 bpm, a SpO₂ accuracy of ±2%, and a temperature accuracy of ±0.5 °C when compared with standard medical devices. The prototype’s affordability, portability, and reliability make it an effective solution for continuous home-based health monitoring, telemedicine services, elderly care, and epidemic surveillance. Future work aims to integrate additional sensors—such as blood pressure and ECG modules—and to utilize cloud-driven analytics for predictive and preventive healthcare applications.

DOI: https://doi.org/10.5281/zenodo.20067343

Published by: vikaspatanker

Authors: A Chenna Kesava Reddy, K Apurupa, K Akhila, N Abhinaya, N Trisha5

Abstract: Cloud storage has emerged as the backbone of modern digital ecosystems, enabling seamless data access, sharing, and collaboration across individuals, enterprises, and government organizations. However, the centralized nature of conventional cloud architectures makes them vulnerable to critical security challenges such as data breaches, manipulation, unauthorized access, and single-point failures. To address these issues, this study proposes a hybrid intelligent cloud security framework that integrates Artificial Intelligence (AI) and Blockchain technologies. Blockchain ensures decentralized trust through cryptographic immutability, distributed consensus, and smart contracts that automate data access and policy enforcement without third-party intervention. Simultaneously, AI specifically Long Short-Term Memory (LSTM) networks is employed for anomaly detection, analysing user activity logs and behavioural patterns to identify irregularities or potential intrusions in real time. The system dynamically adjusts resource allocation and access privileges based on AI-driven insights, enhancing operational efficiency and security adaptability. Experimental evaluation demonstrates that the model achieves high performance in terms of accuracy, precision, recall, F1-score, latency, and throughput, validating its robustness and scalability under varying network conditions. By combining AI’s predictive intelligence with blockchain’s decentralized integrity, the proposed approach delivers a secure, transparent, and self-optimizing cloud storage framework suitable for data-sensitive domains such as healthcare, finance, e-governance, and smart industries.

DOI: https://doi.org/10.5281/zenodo.20067047

Published by: vikaspatanker

Authors: Badisa. Adhilakshmi, Mula Srilatha, Golla Manusri, Bodepudi Tejaswini, Daggubati Maneesha

Abstract: Faking resumes is one of the greatest challenges in the contemporary recruitment systems where most applicants tend to embellish or lie about their academic and professional experience or technical abilities in order to have an advantage in employment. The manual verification systems are tedious, time consuming and they are also subject to error, which makes them ineffective in large-scale hiring. Previous automated systems based on classical machine learning systems like Support Vector Machines (SVM) or Random Forest are only capable of dealing with structured data and do not effectively deal with unstructured, multilingual and complex resumes. The consequences of these limitations are low accuracy, low contextual knowledge and low scalability. To address these issues, in this paper, a hybrid AI-inspired resume verification system incorporating the methods of Natural Language Processing (NLP), deep learning, and classical machine learning will be suggested. The system preprocesses resumes of different types (PDF, DOCX, text) and finds significant data, including education, skills, and experience, and it describes it with contextual embeddings with Transformer-based models. Convolutional Neural Networks (CNNs) are used to capture local linguistic patterns whereas traditional ML models like Random Forest and Gradient Boosting are used to analyse engineered numerical features. An ensemble classifier is a stacked ensemble of these components that is used to give a final score of authenticity, or what percentage probability a resume is a fake resume. The experimental evidence shows that the hybrid model is much better in comparison to traditional methods, as the accuracy of the models is 85-95 with the greatest accuracy of the Transformer based model of 94, and better precision, recall, and F1-score. High-performance, scalable, and automated approach to resume fraud detection through the combination of NLP, deep learning, and classical ML will make recruitment processes more efficient, transparent, and more credible.

DOI: https://doi.org/10.5281/zenodo.20066837

Published by: vikaspatanker